OpenML
John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
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Weka implementation of MultiSearch
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Learner mlr.classif.rpart.tuned from package(s) rpart.
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A RapidMiner Flow
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Automatically created scikit-learn flow.
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Learner mlr.classif.rpart from package(s) rpart.
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Automatically created scikit-learn flow.
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Automatically created MXNet flow.
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Learner mlr.regr.randomForest from package(s) randomForest.
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Automatically created MXNet flow.
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Learner mlr.costsens.classif.ranger from package(s) ranger.
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An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups,…
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Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Linear least squares with l2 regularization. Minimizes the objective function:: ||y - Xw||^2_2 + alpha * ||w||^2_2 This model solves a regression model where the loss function is the linear least…
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Classifier implementing the k-nearest neighbors vote.
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Automatically created keras flow.
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A decision tree classifier.
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A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive…
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Epsilon-Support Vector Regression. The free parameters in the model are C and epsilon. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples…
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Weka implementation of AttributeSelectedClassifier
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Learner classif.rpart from package(s) rpart.
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Learner classif.extraTrees from package(s) extraTrees.
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Learner classif.IBk from package(s) RWeka.
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Learner classif.LiblineaRL1LogReg from package(s) LiblineaR.
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Learner classif.nnTrain from package(s) deepnet.
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Learner classif.rotationForest from package(s) rotationForest.
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No description
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Learner classif.ksvm from package(s) kernlab.
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Learner classif.naiveBayes from package(s) e1071.
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Weka implementation of AttributeSelectedClassifier
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Learner classif.plsdaCaret from package(s) caret.
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Learner classif.lda from package(s) MASS.
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Flow generated by run_task
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Learner mlr.classif.randomForest.filtered from package(s) randomForestSRC, randomForest.
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Learner classif.neuralnet from package(s) neuralnet.
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Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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Learner mlr.classif.ranger from package(s) ranger.
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Weka implementation of FilteredClassifier
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Learner mlr.classif.randomForest from package(s) randomForest.
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Learner mlr.weightedclasses.classif.gbm.preproc from package(s) gbm.
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Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…
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Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…
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Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
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Weka implementation of FilteredClassifier
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Weka implementation of FilteredClassifier
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Weka implementation of CostSensitiveClassifier
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Weka implementation of FilteredClassifier
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Weka implementation of FilteredClassifier
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Weka implementation of REPTree
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Weka implementation of CostSensitiveClassifier
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Weka implementation of CostSensitiveClassifier
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Learner mlr.weightedclasses.classif.xgboost.preproc.overbagged from package(s) xgboost.
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Weka implementation of CostSensitiveClassifier
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Learner mlr.classif.randomForestSRC.preproc from package(s) randomForestSRC.
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Learner mlr.classif.rda.preproc from package(s) klaR.
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Automatically created sub-component.
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Learner mlr.classif.C50.preproc from package(s) C50.
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
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Weka implementation of RandomCommittee
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Weka implementation of RandomTree
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